Wednesday, September 23, 2015

The study of dolphin cognition involves intensive research of animal vocalisations recorded in the field.
In this dissertation I address the automated analysis of audible dolphin communication.
I propose a system called the signal imager that automatically discovers patterns in dolphin signals.
These patterns are invariant to frequency shifts and time warping transformations.
The discovery algorithm is based on feature learning and unsupervised time series segmentation
using hidden Markov models. Researchers can inspect the patterns visually and interactively
run comparative statistics between the distribution of dolphin signals in different behavioral contexts.
The required statistics for the comparison describe dolphin communication as a combination of
the following models: a bag-of-words model, an n-gram model and an algorithm to learn
a set of regular expressions. Furthermore, the system can use the patterns to automatically
tag dolphin signals with behavior annotations. My results indicate that the signal imager provides
meaningful patterns to the marine biologist and that the comparative statistics are aligned with
the biologists’ domain knowledge.